Course support for the DIA module at the ESC Pau

1. Managerial Decision Making
http://DSign4Analytics.com
©2016 L. SCHLENKER
Working with Data
January 25 2017
Prof. M. MINHAJ

2. Data Centre built by the US National Security
Agency in Bluffdale, Utah - capable of storing a
yottabyte of data (that is one thousand trillion
gigabytes)

3. The size of the digital universe, in
terms of the amount of data being
generated, is forecast by IDC to grow
to a staggering 44 zettabytes by
2020.

4. Data Overload….

5. Data in the organization
• Transaction Records
• Documents
Types of Data ?
• Structured
• Semi Structured
• Unstructured

6. Difficulties in Managing Data
 Amount of data increases exponentially.
 Data are scattered and collected by many
individuals using various methods and devices.
 Data come from many sources including
internal sources, personal sources and external
sources.
 Data security, quality and integrity are
critical.

7. Different approaches for management
of Data :
• Conventional file system (use of flat
files)
• DBMS

8. Key Definitions
 Database:
Organized collection of logically related data
 Data:
Stored representations of meaningful objects and
events
 Structured:
Numbers, text, dates
 Unstructured:
Images, video, documents
 Information:
Data processed to increase knowledge in the person
using the data
 Metadata:
Data that describes the properties and context of user
data

9. What is Big Data ?
How big is big data ?

10.  Misconception about big data :
 If it is data and it is big, it is big data
 What is big today may not be big tomorrow
 Big data has attributes that challenge the
current system or business needs.

11. 4 Vs of big data
 Volume
 Velocity
 Variety
 Value

12. Volume
 Machine generated data is produced in much
larger quantities than non-traditional data.
 For example, a single jet engine can
generate 10 TB of data in 30 minutes

13. Velocity
 Social media data streams – while not as
massive as machine-generated data produce
a large influx of opinions and relationships
valuable to customer relationship
management. Even at 140 characters per
tweet, the high velocity (or frequency) of
Twitter data ensures large volumes.

14. Variety
 Traditional data formats tend to be
relatively well described and change slowly.
In contrast, non-traditional data formats
exhibit a dazzling rate of change.

15. Value
 The economic value of different data varies
significantly. Typically there is good
information hidden amongst a larger body of
non-traditional data. The challenge is
identifying what is valuable and then
transforming and extracting that data for
analysis.

16. Data is key to enterprise decision
support, business process
optimization, next best action, and
other initiatives that are vital to
success and growth.

17. Analysing Online Marketing
Campaigns
 Impressions
 Clicks
 CTR

18. Analysing Web Statistics
 Visitors
 Referrals
 Bounce Rate
 Conversions

19. Click Stream Analysis
 Routing
 Stickiness

20. Online reputation management
 Sentiment Analysis

21. Market Basket Analysis
 Association Rules
 Recommendation System

22. Remarketing

23. Fraud Detection
 Data Mining
 Machine Learning

25. Gathering Data from Web Sources
 Powerful Search Engines
 Web Scraping Tools
 Mining Tools

26. Advanced Google
Search Techniques

27. Preparing Data for Analysis using
Spreadsheet Applications (Ex. MS –
Excel)
 Sorting
 Filtering
 Working on missing data
 Eliminating duplicates
 Lookups
 Pivoting

28. Class Activity
Identify the data requirements for a Hotel which is
interested in studying all the aspects of its business. The
management of the hotel intends to use this study to take
appropriate decisions for improving their services.
1. Create a survey form using Google Drive
2. Export the data to MS-Excel
3. Perform Basic Analysis with help of Pivot Table
(Quantitative Data)
4. Identify ways in which qualitative data can be
analyzed.